Spatial Data and Mapping
Grayson White
Math 241
Week 5 | Spring 2026
A simple visual model (Tobler 1994):
This figure:
Red Dots: Cholera cases
Blue Triangles: Pumps
Blue Lines: Closest pump is contained in each section
Q: What pattern do you notice?
Q: What are some possible explanations for the “outliers”? (deaths that don’t match up with the pattern)
Conclusion: Visualizing spatial data can help us see key patterns
Static Maps
Source: JHU
Choropleth Maps
Source: The Guardian
Choropleth Maps
Source: CDC
Choropleth Maps
Source: NYtimes.com
Choropleth Maps
Source: NYtimes.com
Cartogram
Source: Our World in Data
“Hex”bin Maps
Source: FiveThirtyEight
Interactive Maps
Source: NYTimes
What we’ve done so far:
Projections
- We live on a globe.
- We like to create maps on flat pages.
- We have to decide how to map things on a globe to a flat page.
- Enter, projections.
Plotting forested_sf
- Preserves relative distances, rather than just plotting on the x/y plane.
Plotting forested_sf
Implicitly,
geom_sf()looks for a geometry column to set its “geometry” aesthetic to.How can we add a base layer?
Polygon Maps
- Want to draw various boundaries
- EXs: Countries, states, counties, etc
- polygon = closed area including the boundaries making up the area
Adding a base layer to our original map
Solution: use the stringr package!
[1] "Thurston" "Island" "Lewis" "Stevens" "Douglas" "San Juan"
Some mapping suggestions
[1] "Thurston" "Island" "Lewis" "Stevens" "Douglas" "San Juan"# join
left_join(forested_props,
wa,
by = c("county" = "short_name")) %>%
# make R remember this is an sf object
# (R keeps the class of the primary dataset)
st_as_sf() %>%
ggplot(aes(fill = prop_forested)) +
geom_sf() +
scale_fill_distiller(type = "seq",
palette = "Greens",
direction = 1) +
labs(title = "Proportion of forested FIA plots in each county of Washington",
fill = "") +
theme_void() +
theme(legend.position = "right",
legend.key.height = unit(0.9, "in"))
Choropleth Map Showing Median Household Income
Choropleth Map
- How should we modify the code if we don’t want the labels to be colored differently?
Choropleth Map
- Fixes for crowding?
Choropleth Map
- Fixes for crowding?
Choropleth Map
#devtools::install_github("yutannihilation/ggsflabel")
library(ggsflabel)
ggplot(data = wa,
mapping =
aes(geometry = geometry)) +
geom_sf(mapping =
aes(fill = estimate)) +
geom_sf_label_repel(aes(label = short_name),
force = 40) +
scale_fill_distiller(
name = "Median \nHousehold \nIncome",
direction = 1, type = "seq", palette = 4) +
theme_void()
Can Add Information from Another Dataset
library(ggrepel)
ggplot() +
geom_sf(data = wa,
mapping =
aes(geometry = geometry,
fill = estimate)) +
geom_point(data = wa_cities,
mapping = aes(x = long,
y = lat),
shape = 21,
fill = "white",
color = "black") +
geom_label_repel(data = wa_cities,
mapping =
aes(x = long,
y = lat,
label = city)) +
scale_fill_distiller(
name = "Median \nHousehold \nIncome",
direction = 1, type = "seq", palette = 4) +
theme_void()
Different CRS -> Different Looking Map
Cartograms
Scale polygons by a variable.
Cartograms
Often scaled by a population size type variable.
wa_transf_pop <- wa_transf_pop %>%
rename(population_size = value,
median_income = estimate)
wa_transf_pop <- cartogram_cont(
wa_transf_pop,
weight = "population_size"
)
ggplot() +
geom_sf(data = wa_transf_pop,
mapping =
aes(geometry = geometry,
fill = median_income)) +
coord_sf() +
scale_fill_distiller(
name = "Median \nHousehold \nIncome",
direction = 1, type = "seq", palette = 4) +
theme_void()
Hexbin Maps
Static Maps
- What if we want to zoom in?
Alluetian Islands
- Not a great base layer…
- What about dynamic zooming and clickable labels?